Various systems are currently employed to determine elevation values for a particular location, such as a specific street address. Such elevation values are valuable for evaluating risk decisions for insurance and real estate, including flood risk determination and coastal water risk, for enhanced driving and routing instructions, and for estimating drive times. Property and casualty insurance companies use elevation determining systems to understand the elevation of potential and insured properties, for example, properties along the U.S. Gulf Coast and Atlantic seaboard, to perform better hurricane risk assessment. Current solutions for determining elevation values require a latitude and longitude applied to large digital elevation model files, which is a slow and cumbersome process.
The particular location where an elevation value is needed may be established through existing geocoding systems, such as those where geocoding data sets are processed by a geocoding engine that utilizes a textual representation of a location. The engine matches the location, for example, an address, against a data set of geographic data and uses algorithms to determine the location of the input address. The engine returns the location as a coordinate (longitude-latitude pair) referred to as a geocode and, depending on the system, may also return a more complete and accurate address based on an address hygiene function. Geocoding data sets used for the above purposes include point level or parcel level geographic data sets and street segment line geographic data sets. While point or parcel level data sometimes include elevation value data for the address location, street segment data sets do not include elevation value data and various techniques are employed to obtain elevation values when needed.
The U.S. Geological Survey (USGS) and other data providers have compiled large digital elevation models for the United States. The compiled digital data models from the USGS are compiled using a 30-meter grid for the entire United States, where each 30-meter grid (approximately 100 feet by 100 feet) has the elevation value for the center point of the 30-meter square. The center point elevation value is provided to an accuracy of approximately 1/10 of a meter. This USGS digital elevation model is provided in a very large data set of elevation values, which is typically over forty gigabytes (40 GB) of data. Accordingly, to take a specific location and match it against the data set to obtain an elevation for the location, it is necessary to apply it against the entire massive data set.
In attempts to reduce the amount of data that has to be processed, the data set of elevation values is subdivided, for example, into one degree by one degree (1°×1°) sectors of latitude and longitude of 30-meter square elevation values. The user enters an address or location and a determination is made as to which one degree by one degree (1°×1°) sector the address falls into. Thereafter, that specific sector is searched for the elevation value. The specific portions of the digital elevation model areas or sectors vary in size with different solutions and different digital elevation models employed in various systems. This process is a two-step process. First the latitude and longitude of the particular address or location is employed to make a determination as to which sector of the elevation digital model includes the location. Thereafter, the specific sector is searched for the needed location elevation value. The solutions for implementing this two-step process are usually performed as a web service or service-oriented architecture that is performed on a pay-for-use basis. This requires that the data or files to be processed be sent outside the user organization. The data or files are sent to the service so that the data or file can be applied against the digital elevation data set to generate the needed elevation information for the specific locations, which are then returned to the user.
While the above solutions works satisfactorily for certain applications, they may be slow and cumbersome and/or may require that data or files, which may contain sensitive business or other confidential information such as the property address of interest to a particular insurance company, be sent outside of the user's organization. It is, thus, desirable to have an efficient and effective elevation solution that can be implemented internally on a user network, without undue operating burden to the user's computing system.